• Explore genetic signatures, genealogy and phenotypic changes of soybean breeding
• Ultimate task is to understand how the genome changed with the breeding of lines, and how this affected other traits

• Next-generation sequencing DNA-seq on 79 lines: DNA sequencing libraries were prepared using TruSeq DNA sample prep and NuGENs unamplified prep kits (Illumina Inc., San Diego, CA and NuGEN Technologies Inc., San Carlos, CA).
• Field yield trials: 30/79 + 138 ancestral lines
• Breeding literature, what lines were bred to produce what line

• Copy number variation (CNV): 2Gb of analysis files, annotations
• SNPs: 12Gb of data, 20mill SNPs, 1mil locations, 79 lines
• Genealogy: Shows the parent to child lineage

• Seven tabs containing different functionality
• Four of the tabs, CNV Location, Copy Number, "Search CNVs by Location", and CNV List, primarily concerned with exploring the identified copy number variants
• The other three tabs, Phenotype Data, Genealogy, and Methodology provide additional information about the soybean cultivars and the experimental methodology
• App written by Dr Susan Vanderplas

• shiny: provides the infrastructure, web interface
• ggplot2: static plots
• animint: interactive plots

What does copy number variation mean?

Open up the copy number variation (CNV) app on your computer http://shiny.soybase.org/CNV/

or

http://srvanderplas.com/Shiny/SoybeanApp/CNV/

(It might be best to share one computer between three or four people, so that the servers don't get overloaded.)

• Has yield increased over the years?
• What is the relationship between yield and protein?
• What is the name of the line that is a recent development, and was sequenced in the study?
• Find a line who's yield is lower than both parents.

(Hint: Use the Field Trials tab.)

• Is Conrad a descendant of Essex?
• Who are the grandparents of Essex?
• How is Illini related to Lawrence?

(Hint: Use the Genealogy tab.)

Find some locations on the genome that Lawrence might have inherited from Illini.

(Hint: Use the Barcode plots.)

• Five tabs containing different functionality
• Overview, SNP locations, search by id, SNP browser, SNP distance and the Kevin Bacon distance
• App written by Susan Vanderplas

• Exploring the answer space for a theoretical question: https://erichare.shinyapps.io/pelica/
• Getting an image and the code for a logo plot: https://erichare.shinyapps.io/gglogo/

• Finding a statistical answer to a matching problem: http://erichare.shinyapps.io/x3prplus

• Author: Eric Hare

https://erichare.shinyapps.io/pelica/

• interactive tool to help plan an experiment
• What is the baseline probability that a peptide of length p is included in a synthetic library of size N (really large)?
• What role does peptide encoding play?

https://erichare.shinyapps.io/gglogo/

https://erichare.shinyapps.io/x3prplus/

Pick one of the three shiny apps and explore it for yourself:

• peptide libraries: http://erichare.shinyapps.io/pelica
• logo plot: http://erichare.shinyapps.io/gglogo
• bullet matching: http://erichare.shinyapps.io/x3prplus

• Cheat sheet
• ggplot2: Elegant Graphics for Data Analysis, Hadley Wickham, web site
• R Graphics Cookbook, Winston Chang
• Q/A site: http://stackoverflow.com
• Dynamic Documents with R and knitr, Yihui Xie,
• Naomi Robbins, Creating More Effective Graphs
• Antony Unwin, Graphical Data Analysis with R

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